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Bayesian structural equation modeling

Bayesian structural equation modeling (Loan 2 times)

Material type
단행본
Personal Author
DePaoli, Sarah, author.
Title Statement
Bayesian structural equation modeling / Sarah DePaoli.
Publication, Distribution, etc
New York, NY :   The Guilford Press,   c2021.  
Physical Medium
xxvi, 521 p. : ill. ; 27 cm.
Series Statement
Methodology in the social sciences
ISBN
9781462547746 (cloth)
요약
"This book is meant as a guide for implementing Bayesian methods for latent variable models. I have included thorough examples in each chapter, highlighting problems that can arise during estimation, potential solutions, and guides for how to write up findings for a journal article. This book is structured into 12 main chapters, beginning with introductory chapters comprising Part I. Part II is comprised of Chapters 3-5. Each of these chapters deals with various models and techniques related to measurement models within SEM. Part III contains Chapters 6-7, on extending the structural model. Part IV contains Chapters 8-10, on longitudinal and mixture models. Finally, Part IV contains chapters that discuss special topics"--
Bibliography, Etc. Note
Includes bibliographical references (p. 482-498) and indexes.
Subject Added Entry-Topical Term
Bayesian statistical decision theory. Social sciences --Statistical methods.
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020 ▼a 9781462547746 (cloth)
035 ▼a (KERIS)REF000019554911
040 ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009
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050 0 0 ▼a BF39.2.B39 ▼b D46 2021
082 0 0 ▼a 150.1/519542 ▼2 23
084 ▼a 150.1519542 ▼2 DDCK
090 ▼a 150.1519542 ▼b D419b
100 1 ▼a DePaoli, Sarah, ▼e author.
245 1 0 ▼a Bayesian structural equation modeling / ▼c Sarah DePaoli.
260 ▼a New York, NY : ▼b The Guilford Press, ▼c c2021.
264 1 ▼a New York, NY : ▼b The Guilford Press, ▼c 2021.
300 ▼a xxvi, 521 p. : ▼b ill. ; ▼c 27 cm.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a unmediated ▼b n ▼2 rdamedia
338 ▼a volume ▼b nc ▼2 rdacarrier
490 1 ▼a Methodology in the social sciences
504 ▼a Includes bibliographical references (p. 482-498) and indexes.
520 ▼a "This book is meant as a guide for implementing Bayesian methods for latent variable models. I have included thorough examples in each chapter, highlighting problems that can arise during estimation, potential solutions, and guides for how to write up findings for a journal article. This book is structured into 12 main chapters, beginning with introductory chapters comprising Part I. Part II is comprised of Chapters 3-5. Each of these chapters deals with various models and techniques related to measurement models within SEM. Part III contains Chapters 6-7, on extending the structural model. Part IV contains Chapters 8-10, on longitudinal and mixture models. Finally, Part IV contains chapters that discuss special topics"-- ▼c Provided by publisher.
520 ▼a "This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and excerpts of annotated code in both Mplus and R. The companion website supplies datasets, code, and output for all of the book's examples. "-- ▼c Provided by publisher.
650 0 ▼a Bayesian statistical decision theory.
650 0 ▼a Social sciences ▼x Statistical methods.
830 0 ▼a Methodology in the social sciences.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/Western Books/ Call Number 150.1519542 D419b Accession No. 111854384 Availability In loan Due Date 2022-02-09 Make a Reservation Available for Reserve R Service M

Contents information

Table of Contents

Preface
I. Introduction
1. Background
1.1. Bayesian Statistical Modeling: The Frequency of Use
1.2. The Key Impediments within Bayesian Statistics
1.3. Benefits of Bayesian Statistics within SEM
1.3.1. A Recap: Why Bayesian SEM?
1.4. Mastering the SEM Basics: Precursors to Bayesian SEM
1.4.1. The Fundamentals of SEM Diagrams and Terminology
1.4.2. LISREL Notation
1.4.3. Additional Comments about Notation
1.5. Datasets used in the Chapter Examples
1.5.1. Cynicism Data
1.5.2. Early Childhood Longitudinal Survey-Kindergarten Class
1.5.3. Holzinger and Swineford (1939)
1.5.4. IPIP 50: Big Questionnaire
1.5.5. Lakaev Academic Stress Response Scale
1.5.6. Political Democracy
1.5.7. Program for International Student Assessment
1.5.8. Youth Risk Behavior Survey
2. Basic Elements of Bayesian Statistics
2.1. A Brief Introduction to Bayesian Statistics
2.2. Setting the Stage
2.3. Comparing Frequentist and Bayesian Inference
2.4. The Bayesian Research Circle
2.5. Bayes'' Rule
2.6. Prior Distributions
2.6.1. The Normal Prior
2.6.2. The Uniform Prior
2.6.3. The Inverse Gamma Prior
2.6.4. The Gamma Prior
2.6.5. The Inverse Wishart Prior
2.6.6. The Wishart Prior
2.6.7. The Beta Prior
2.6.8. The Dirichlet Prior
2.6.9. Different Levels of Informativeness for Prior Distributions
2.6.10. Prior Elicitation
2.6.11. Prior Predictive Checking
2.7. The Likelihood (Frequentist and Bayesian Perspectives)
2.8. The Posterior
2.8.1. An Introduction to Markov Chain Monte Carlo Methods
2.8.2. Sampling Algorithms
2.8.3. Convergence
2.8.4. MCMC Burn-in Phase
2.8.5. The Number of Markov Chains
2.8.6. A Note about Starting Values
2.8.7. Thinning a Chain
2.9. Posterior Inference
2.9.1. Posterior Summary Statistics
2.9.2. Intervals
2.9.3. Effective Sample Size
2.9.4. Trace-plots
2.9.5. Autocorrelation Plots
2.9.6. Posterior Histogram and Density Plots
2.9.7. HDI Histogram and Density Plots
2.9.8. Model Assessment
2.9.9. Sensitivity Analysis
2.10. A Simple Example
2.11. Chapter Summary
2.11.1. Major Take Home Points
2.11.2. Notation Referenced
2.11.3. Annotated Bibliography of Select Resources
Appendix A: Getting Started with R
II. Measurement Models and Related Issues
3. The Confirmatory Factor Analysis Model
3.1. Introduction to Bayesian CFA
3.2. The Model and Notation
3.2.1. Handling Indeterminacies in CFA
3.3. The Bayesian Form of the CFA Model
3.3.1. Additional Information about the (Inverse) Wishart Prior
3.3.2. Alternative Priors for Covariance Matrices
3.3.3. Alternative Priors for Variances
3.3.4. Alternative Priors for Factor Loadings
3.4. Example: Basic Confirmatory Factor Analysis Model
3.5. Example: Implementing Near-Zero Priors for Cross-Loadings
3.6. How to Write up Bayesian CFA Results
3.6.1. Hypothetical Data Analysis Plan
3.6.2. Hypothetical Results Section
3.6.3. Discussion Points Relevant to the Analysis
3.7. Chapter Summary
3.7.1. Major Take Home Points
3.7.2. Notation Referenced
3.7.3. Annotated Bibliography of Select Resources
3.7.4. Example Code for Mplus
3.7.5. Example Code for R
4. Multiple Group Models
4.1. A Brief Introduction to Multi-Group Models
4.2. Introduction to the Multiple-Group CFA Model (with Mean Differences)
4.3. The Model and Notation
4.4. The Bayesian Form of the Multiple-Group CFA Model
4.5. Example: Using a Mean Differences, Multiple-Group CFA Model to Assess for School Differences
4.6. Introduction to the MIMIC Model
4.7. The Model and Notation
4.8. The Bayesian Form of the MIMIC Model
4.9. Example: Using the MIMIC Model to Assess for School Differences
4.10. How to Write up Bayesian Multiple-Group Model Results with Mean Differences
4.10.1. Hypothetical Data Analysis Plan
4.10.2. Hypothetical Results Section
4.10.3. Discussion Points Relevant to the Analysis
4.11. Chapter Summary
4.11.1. Major Take Home Points
4.11.2. Notation Referenced
4.11.3. Annotated Bibliography of Select Resources
4.11.4. Example Code for Mplus
4.11.5. Example Code for R
5. Measurement Invariance Testing
5.1. A Brief Introduction to Measurement Invariance in SEM
5.1.1. Stages of Traditional MI Testing
5.1.2. Challenges Within Traditional MI Testing
5.2. Bayesian Approximate MI
5.3. The Model and Notation
5.4. Priors within Bayesian Approximate MI
5.5. Example: Illustrating Bayesian Approximate MI for School Differences
5.5.1. Results for the Conventional MI Tests
5.5.2. Results for the Bayesian Approximate MI Tests
5.5.3. Results Comparing Latent Means Across Approaches
5.5.4. Hypothetical Data Analysis Plan
5.6. How to Write up Bayesian Approximate Measurement Invariance Results
5.6.1. Analytic Procedure
5.6.2. Results
5.6.3. Discussion Points Relevant to the Analysis
5.7. Chapter Summary
5.7.1. Major Take Home Points
5.7.2. Notation Referenced
5.7.3. Annotated Bibliography of Select Resources
5.7.4. Example Code for Mplus
5.7.5. Example Code for R
III. Extending the Structural Model
6. The General Structural Equation Model
6.1. Introduction to Bayesian SEM
6.2. The Model and Notation
6.3. The Bayesian form of SEM
6.4. Example: Revisiting Bollen''s (1989) Political Democracy Example
6.4.1. Motivation for this Example
6.4.2. The Current Example
6.5. How to Write up Bayesian SEM Results
6.5.1. Hypothetical Data Analysis Plan
6.5.2. Hypothetical Results Section
6.5.3. Discussion Points Relevant to the Analysis
6.6. Chapter Summary
6.6.1. Major Take Home Points
6.6.2. Notation Referenced
6.6.3. Annotated Bibliography of Select Resources
6.6.4. Example Code for Mplus
6.6.5. Example Code for R
Appendix A: Causal Inference and Mediation Analysis
7. Multilevel Structural Equation Modeling
7.1. Introduction to Multilevel SEM
7.1.1. MSEM Applications
7.1.2. Contextual Effects
7.2. Extending MSEM into the Bayesian Context
7.3. The Model and Notation
7.4. The Bayesian form of MSEM
7.5. Example: A 2-Level CFA with Continuous Items
7.5.1. Implementation of Example
7.5.2. Example Results
7.6. Example: A Three-Level CFA with Categorical Items
7.6.1. Implementation of Example
7.6.2. Example Results
7.7. How to Write up Bayesian MSEM Results
7.7.1. Hypothetical Data Analysis Plan
7.7.2. Hypothetical Results Section
7.7.3. Discussion Points Relevant to the Analysis
7.8. Chapter Summary
7.8.1. Major Take Home Points
7.8.2. Notation Referenced
7.8.3. Annotated Bibliography of Select Resources
7.8.4. Example Code for Mplus
7.8.5. Example Code for R
IV. Longitudinal and Mixture Models
8. Latent Growth Curve Modeling
8.1. Introduction to Bayesian LGCM
8.2. The Model and Notation
8.2.1. Extensions of the LGCM
8.3. The Bayesian Form of the LGCM
8.3.1. Alternative Priors for the Factor Variances and Covariances
8.4. Example: Bayesian Estimation of the LGCM using ECLS-K Reading Data
8.5. Example: Extending the Example to Include Separation Strategy Priors
8.6. Example: Extending the Framework to Assessing Measurement Invariance Over Time
8.7. How to Write up Bayesian LGCM Results

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